Is reinforcement learning needed to create Strong AI?
Motivational Theory Versus Learning Theory

Rewards and punishment are based on human motivational theory and are broad generalizations of the neurological factors in mental growth.  At a neurological level, there are chemically based signals, such as seratonin, dopamine, and oxytocin.  Some of these have their own pathways in the brain.

There are also pain signals arising from the body and internally generated stimulatory and inhibitory signals in chemical and electro-chemical forms.  There is neuroplasticity, where the brain circuits form new topologies.  The hippocampus is a structure known to be important in learning.1  The study of learning in the brain is fervent, but the object of study is complex.  Even so, every year, progress is made.

Multiple Dimensions or Singly Quantifiable Intelligence

Conjecture about strength versus weakness of intelligence may be of limited use.  There are two valid criticisms discussed in other Q&amp;A here in more detail.


Twenty-two independent genes have been identified that correlate to typical academic intelligence quantification in humans, rational and linguistic, eleven of which were identified in 2017.2  There are likely more.  Creative, athletic, emotional, and leadership qualities have not yet been captured in standardized intelligence testing, so it is likely that additional genes related to those abilities exist.  The notion that intelligence can be accurately aggregated into a single g-factor is weakly supported, if it were true, implies that all twenty-two genes affect only one magical function.  Statistically, such alignment is close to impossible.
Intelligence is only meaningful in context.  Fast learning in one context appears like mental brilliance, but in another context the same learning capability may be dysfunctional.  


Reinforcement in the Larger Pool of AI Research

Reinforcement Learning is not a requirement for AI as far as is currently known.  Reinforcement is a mathematical strategy for driving a set of behavioral parameters toward a defined optimum.  Although its variants work well for many applications, it is not closely aligned with any of the features of the hippocampus, the limbic system, the cerebral cortex, or any other learning systems in the human brain. 

Even though there is still much to learn about brain workings, the success of reinforcement learning, rule based systems, convolution, attention based networks, and generative networks provides information about what is possible in computers and what may or may not be a mechanism of the brain.

What Reward and Punishment Imply

The existence of rewards and punishment imply the existence of favorable and unfavorable family, workplace, or community states.  If the term world in the question is referring to the immediately related environment, then the sentence is probably true, but it may be misleading without clarification.

World states are based on economics which involve the minds of many people in community, aggressive actions, resource acquisition, consumption, cultural perspectives, and many other factors.  A single individual's perception of world states is subjective.

Defying Motivational Mechanisms and Intelligence

The same person on two different days may have different ideas of what is favorable or unfavorable, which may or may not correlate to reward and punishment motivations.  The words rebellion and indifference describe states where external reward and punishment produce opposing or independent results in learned behavior respectively.  Some of the most intelligent behavior in humans were of these kinds.

What drives the classification of external states within a single individual is something not well understood.  The neurological factors involved in the emergence of opposition to pain, pleasure, reward, and punishment is an interesting area of study and certainly related to AI.

Genetic factors involved in such contrary learning are likely to fall outside the twenty-two genes, since those genes correlated with standardized academic testing.  Academia is about conformance.  If we don't agree with the author of the textbook for a course, we pretend we do when we take the test if we want a high grade.

World-changing intelligence, such as that of Ghandi's nonviolent opposition, Isaac Newton's invention of physics, the furtherance of logical disproof by Socrates, or whoever dared to learn how to light a fire. is always the exhibition of classifying in a way that contradicts that of the surrounding culture.

Discovery and Acceptance in Group Learning

This is even true of project-changing intelligence.  A software engineer might say, "That software will never work because the database schema indicates a one to many relationship between companies and office addresses, but it is a many to many relationship."  Such a statement doesn't change the world and may completely lack group acceptance when spoken.  Later in the project, after the statement is fully accepted, the reclassification changes the design so that the project is not caught in a well of wasted coding.

Interestingly, convincing the others often falls to someone other than the originator of the idea &mdash; someone else on the team that has a different set of mental abilities.

It is true that classification of what is favorable or unfavorable is key, but how that classification exhibits intelligence is not well understood.

What Benefits AI Development

Although not a requirement, a solid approach to AI development is discovery, from the directions of multiple fields of study, of what intelligence is.  Some say we know what it is now, but most definitions are largely expressions of historical inaccuracies.

Clarity about what intelligence is and what it is expected to be in such a way that it can be represented mathematically is needed.  Only then can we better achieve it in humans and machines.  Sources of understanding may come from discovery in any area of study, such as these.


Evidence based psychology
Evidence based genetics
Evidence based neurological research
Evidence based artificial systems design


There is a growing trend to hack AI, but playing with AI as if it were a game might be a formula for creating Frankenstein.  A useful and collaborative collection of AI devices in robots, autonomous vehicles, and data centers will require interdisciplinary inquiry, experimentation, and engineering.  The description of this site implies that ethics should be involved, and that's a smart conception.

With a purview that crosses divisions in study and practice, central themes and models of intelligence are likely to develop.  This question is on the right track in this respect. It asks questions without accepting too quickly conjecture  &mdash; ideas and hypotheses presented but not yet shown to be true.

What is Known

That computer simulation or replication of human abilities have already been achieved and that such achievements indicate AI to be a reasonable long term objective few would deny.  Certainly in the areas of arithmetic, high speed machine control, sorting of addressed parcels, and many other roles, computers far outperform humans.  Whether world-changing intelligence will emerge in artificial systems is hoped for by some, feared by others, and asserted as inevitable by those who seek funding to prove it.

References

[1] Training your brain: Do mental and physical (MAP) training enhance cognition through the process of neurogenesis in the hippocampus?, D.M. Curlik and T.J. Shors, 2012

[2] Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence, Suzanne Sniekers et. al., 2017